Hybrid Virtual- and Field Work-based Service Learning with Green Information Technology and Systems Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional engineering service learning (SL) projects can be classified as: 1) collaborations with a community group or non-profit organization to provide specific engineering around a community need, or 2) an internship-like experience with industry to address work requested by a client. The limitation of both traditional SL approaches is that they do not prepare students to implement unprescribed projects. In contrast, here students chose both the project and the partner for a self-directed engineering SL experience. This paper presents the findings of this novel pedagogical exercise in which students acted as change agents for industry by implementing unsolicited energy conservation measures (ECMs) focused on green information technology and systems (IT/S), in order to improve organizations’ environmental and economic performance. The hybrid SL projects had both ‘virtual’ and ‘real’ (field-work) SL components. For the virtual component, student teams developed and published on-line, open-source ECM calculators. For the field-work component, the teams self-selected industry clients and performed IT/S energy audits. Applicable ECMs were then selected and tailored, forming the basis of recommendations to the organizations. Results demonstrate the effectiveness of such hybrid engineering SL projects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it